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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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systems. Responsibilities include: Integration of advanced sensing technologies with continuum robotic systems and development of associated sensing and estimation algorithms Path planning based on medical
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will: Develop and implement model-based and data-driven (AI) optimization algorithms for battery charging Integrate physics-informed models and data-driven tools to design health-aware charging protocols
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approaches, such as topology optimization, with gradient-free methods, such as genetic algorithm, in order to exploit the specific strengths of the two classes of algorithms, and through this reach an ideal
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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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fellow will conduct research on Algorithmic Verification of Concurrent Systems within the Programming Languages, Logic, and Software Security Research Group at Aarhus University. The focus of the position
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The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Centre for Biosustainability (DTU Biosustain) is developing novel methods to engineer proteins more effectively using
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Job Description The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Centre for Biosustainability (DTU Biosustain) is developing novel methods to engineer proteins more